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Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

Bruno Bouchard, Kevin Bouchard, Noam Brown, Niyati Chhaya, Eitan Farchi, Sebastien Gaboury, Christopher Geib, Amelie Gyrard, Kokil Jaidka, Sarah Keren, Roni Khardon, Parisa Kordjamshidi (+19 others)
2018 The AI Magazine  
This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the  ...  The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI.  ...  This workshop follows the success of previous health-related AAAI workshops, including those focused on personalized and population healthcare, and the first joint workshop on health intelligence at AAAI  ... 
doi:10.1609/aimag.v39i4.2823 fatcat:lkeg3klhhnawzemryzhbnjlvlm

Page 305 of Computational Linguistics Vol. 30, Issue 3 [page]

2004 Computational Linguistics  
In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-2000), Austin, TX, July 30-August 3, pages 679-684. Biber, Douglas. 1993.  ...  In Proceedings of the Second National Conference on Artificial Intelligence (AAAI-82), Pittsburgh, August 18-20, pages 265-268. Everitt, Brian S. 1977. The Analysis of Contingency Tables.  ... 

Page 27 of Computational Linguistics Vol. 32, Issue 1 [page]

2006 Computational Linguistics  
Wordnet:: similarity measuring the relatedness of concepts. In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-04).  ...  Using measures of semantic relatedness for word sense ee ation. ms Proceedings of the Fourth International Conference on Intelligent Text Processing and (¢ ae R Mexico ¢ Pedersen nitational Linguistics  ... 

The Permutation in a Haystack Problem and the Calculus of Search Landscapes

Vincent A. Cicirello
2016 IEEE Transactions on Evolutionary Computation  
In Proceedings of the Nineteenth National Conference on Artificial Intelligence and the Sixteenth Innovative Applications of Artificial Intelligence Conference, pages 1004-1005.  ...  Coverage of AAAI 2005 Out- standing Paper Award. 12/22/2005 AI Magazine (Winter 2005): "The Twentieth National Conference on Artificial Intelligence" (M. Veloso and S.  ... 
doi:10.1109/tevc.2015.2477284 fatcat:u4xrhgz4yzfkvkicbvuuto4qkq

References [chapter]

2010 The Handbook of Computational Linguistics and Natural Language Processing  
Sumita (2002) , Using language and translation models to select the best among outputs from multiple MT systems.  ...  Fourth Workshop on the Semantics and Pragmatics of Dialogue, 43-50. Bos, Johan, & Tetsush Oka (2002), An inference-based approach to dialogue system design. Proceedings of the 19th COLING, 113-19.  ...  Bos, Johan, Ewan Klein, Oliver Lemon, & Tetsushi Oka (2003), DIPPER: description and formalisation of an information-state update dialogue system architecture.  ... 
doi:10.1002/9781444324044.refs fatcat:udzjhccz6vg4hnsy5h744y67ai

Ontologies and Data Management: A Brief Survey

Thomas Schneider, Mantas Šimkus
2020 Künstliche Intelligenz  
This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.  ...  In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology.  ...  original author(s) and the source,  ... 
doi:10.1007/s13218-020-00686-3 pmid:32999532 pmcid:PMC7497697 fatcat:zeskjfjrpvcojjh6q3yjzhlyci

What do people study when they study Twitter? Classifying Twitter related academic papers

Shirley A. Williams, Melissa M. Terras, Claire Warwick
2013 Journal of Documentation  
Findings The majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users.  ...  The study is focussed on microblogging, the applicability of the approach to other media is not considered.  ...  #hardtoparse: POS tagging and parsing the Twitterverse 2011 AAAI Workshop --Technical Report WS--11--05 20 25 133 Fox B.I., Varadarajan R.  ... 
doi:10.1108/jd-03-2012-0027 fatcat:3rfuptki5bgxvfxumtf6ea6vxi

Kernel Mean Embedding of Distributions: A Review and Beyond

Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Bernhard Schölkopf
2017 Foundations and Trends® in Machine Learning  
The embedding of distributions enables us to apply RKHS methods to probability measures which prompts a wide range of applications such as kernel two-sample testing, independent testing, and learning on  ...  The survey begins with a brief introduction to the RKHS and positive definite kernels which forms the backbone of this survey, followed by a thorough discussion of the Hilbert space embedding of marginal  ...  In Proceedings of the 27th AAAI Conference on Artificial Intelligence, pages 1660-1661. AAAI Press, 2013. G. Doran, K. Muandet, K. Zhang, and B. Schölkopf.  ... 
doi:10.1561/2200000060 fatcat:vgmsbodozngltpzy6c2idxnx34

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations [article]

Qian Li, Hao Peng, Jianxin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang
2021 arXiv   pre-print
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.  ...  It then uses the newly obtained information to improve the decisions of previously extracted arguments.  ...  Nguyen, “One for all: Neural joint modeling of Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational entities and events,” in The Thirty-Third AAAI Conference on Artificial  ... 
arXiv:2106.12384v2 fatcat:blyylym77vdupbrolil2dtmrna

Logic and Learning (Dagstuhl Seminar 19361)

Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, Daniel Neider, Michael Wagner
2020 Dagstuhl Reports  
We summarise the motivations and proceedings of the seminar, and report on the abstracts of the talks and the results of the breakout sessions.  ...  This report documents the efforts of Dagstuhl Seminar 19361 on "Logic and Learning" to bring these communities together in order to: (i) bridge the research efforts between them and foster an exchange  ...  The format of the seminar including ample time for discussions and breakout sessions received positive feedback from the participants.  ... 
doi:10.4230/dagrep.9.9.1 dblp:journals/dagstuhl-reports/BenediktKKN19 fatcat:rwjks5mydzhctlvedtel3vtzoy

A cross-benchmark comparison of 87 learning to rank methods

Niek Tax, Sander Bockting, Djoerd Hiemstra
2015 Information Processing & Management  
In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets.  ...  Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task.  ...  In Proceedings of the 25th AAAI Conference on Artificial Intelligence. Wang, S., Ma, J., and Liu, J. (2009b).  ... 
doi:10.1016/j.ipm.2015.07.002 fatcat:vityxuoyxzfezhdizq7sfofwka

Unsupervised Text Summarization via Mixed Model Back-Translation [article]

Yacine Jernite
2019 arXiv   pre-print
We present several initial models which rely on the asymmetrical nature of the task to perform the first back-translation step, and demonstrate the value of combining the data created by these diverse  ...  Our system outperforms the current state-of-the-art for unsupervised sentence summarization from fully unaligned data by over 2 ROUGE, and matches the performance of recent semi-supervised approaches.  ...  In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelli- gence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational  ... 
arXiv:1908.08566v1 fatcat:nx5i2l3bhbhnjb4n3hzp2gzkai

What to Pre-Train on? Efficient Intermediate Task Selection [article]

Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, Iryna Gurevych
2021 arXiv   pre-print
With an abundance of candidate datasets as well as pre-trained language models, it has become infeasible to run the cross-product of all combinations to find the best transfer setting.  ...  Our results show that efficient embedding based methods that rely solely on the respective datasets outperform computational expensive few-shot fine-tuning approaches.  ...  We thank Leonardo Ribeiro and the anonymous reviewers for insightful feedback and suggestions on a draft of this paper.  ... 
arXiv:2104.08247v2 fatcat:4ljcfshev5f3tmgugrrrkh3s4m

In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers [article]

Vivek Krishna Pradhan, Mike Schaekermann, Matthew Lease
2021 arXiv   pre-print
In Stage 2 (RESOLVE), the requester selects one or more of these ambiguous examples to label (resolving ambiguity).  ...  We report image labeling experiments over six task designs using Amazon's Mechanical Turk.  ...  Journal of Artificial Intelligence Research (JAIR) 69, 143–189. Conference Award Track. Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., et al. (2008).  ... 
arXiv:2112.02255v1 fatcat:xpvsuclbi5dfrmfm2ejx54qdii

Legal and Technical Feasibility of the GDPR's Quest for Explanation of Algorithmic Decisions: of Black Boxes, White Boxes and Fata Morganas

Maja BRKAN, Grégory BONNET
2020 European Journal of Risk Regulation  
Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term "explainable AI (XAI)".  ...  On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject  ...  MG Core et al, "Building Explainable Artificial Intelligence Systems" (2006) AAAI Conference on Artificial Intelligence 1766, p 1768. 120 M Harbers et al, "Design and Evaluation of Explainable BDI Agent  ... 
doi:10.1017/err.2020.10 fatcat:iwu7o2qa5zah5ge3rxxzrpcstu
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